


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
						import transformers
def 							UpperCamelCase_(					_A :int	)->		List[str]:
			random.seed(_A	)
			np.random.seed(_A	)
			torch.manual_seed(_A	)
			torch.cuda.manual_seed_all(_A	)
			# ^^ safe to call this function even if cuda is not available







class 							lowerCamelCase__						:






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case = 0.9999   ,    snake_case = 0.0   ,    snake_case = 0   ,    snake_case = False   ,    snake_case = 1.0   ,    snake_case = 2 / 3   ,    snake_case = None   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						if isinstance(snake_case   ,    torch.nn.Module  ):
									UpperCamelCase__       =							(
									    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
									    "Please pass the parameters of the module instead."
									)
									deprecate(
									    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"   ,    "1.0.0"   ,    snake_case   ,    standard_warn=snake_case   ,    )
									UpperCamelCase__       =							parameters.parameters()

									# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
									UpperCamelCase__       =							True

						if kwargs.get("max_value"   ,    snake_case  ) is not None:
									UpperCamelCase__       =							"The `max_value` argument is deprecated. Please use `decay` instead."
									deprecate("max_value"   ,    "1.0.0"   ,    snake_case   ,    standard_warn=snake_case  )
									UpperCamelCase__       =							kwargs["max_value"]

						if kwargs.get("min_value"   ,    snake_case  ) is not None:
									UpperCamelCase__       =							"The `min_value` argument is deprecated. Please use `min_decay` instead."
									deprecate("min_value"   ,    "1.0.0"   ,    snake_case   ,    standard_warn=snake_case  )
									UpperCamelCase__       =							kwargs["min_value"]

						UpperCamelCase__       =							list(snake_case  )
						UpperCamelCase__       =							[p.clone().detach() for p in parameters]

						if kwargs.get("device"   ,    snake_case  ) is not None:
									UpperCamelCase__       =							"The `device` argument is deprecated. Please use `to` instead."
									deprecate("device"   ,    "1.0.0"   ,    snake_case   ,    standard_warn=snake_case  )
									self.to(device=kwargs["device"]  )

						UpperCamelCase__       =							None

						UpperCamelCase__       =							decay
						UpperCamelCase__       =							min_decay
						UpperCamelCase__       =							update_after_step
						UpperCamelCase__       =							use_ema_warmup
						UpperCamelCase__       =							inv_gamma
						UpperCamelCase__       =							power
						UpperCamelCase__       =							0
						UpperCamelCase__       =							None  # set in `step()`

						UpperCamelCase__       =							model_cls
						UpperCamelCase__       =							model_config




			@classmethod
			def 					snake_case__					(				cls   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							model_cls.load_config(snake_case   ,    return_unused_kwargs=snake_case  )
						UpperCamelCase__       =							model_cls.from_pretrained(snake_case  )

						UpperCamelCase__       =							cls(model.parameters()   ,    model_cls=snake_case   ,    model_config=model.config  )

						ema_model.load_state_dict(snake_case  )
						return ema_model




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if self.model_cls is None:
									raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."  )

						if self.model_config is None:
									raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."  )

						UpperCamelCase__       =							self.model_cls.from_config(self.model_config  )
						UpperCamelCase__       =							self.state_dict()
						state_dict.pop("shadow_params"   ,    snake_case  )

						model.register_to_config(**snake_case  )
						self.copy_to(model.parameters()  )
						model.save_pretrained(snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							max(0   ,    optimization_step - self.update_after_step - 1  )

						if step <= 0:
									return 0.0

						if self.use_ema_warmup:
									UpperCamelCase__       =							1 - (1 + step / self.inv_gamma) ** -self.power
						else:
									UpperCamelCase__       =							(1 + step) / (10 + step)

						UpperCamelCase__       =							min(snake_case   ,    self.decay  )
						# make sure decay is not smaller than min_decay
						UpperCamelCase__       =							max(snake_case   ,    self.min_decay  )
						return cur_decay_value




			@torch.no_grad()
			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if isinstance(snake_case   ,    torch.nn.Module  ):
									UpperCamelCase__       =							(
									    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
									    "Please pass the parameters of the module instead."
									)
									deprecate(
									    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"   ,    "1.0.0"   ,    snake_case   ,    standard_warn=snake_case   ,    )
									UpperCamelCase__       =							parameters.parameters()

						UpperCamelCase__       =							list(snake_case  )

						self.optimization_step += 1

						# Compute the decay factor for the exponential moving average.
						UpperCamelCase__       =							self.get_decay(self.optimization_step  )
						UpperCamelCase__       =							decay
						UpperCamelCase__       =							1 - decay

						UpperCamelCase__       =							contextlib.nullcontext
						if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									import deepspeed

						for s_param, param in zip(self.shadow_params   ,    snake_case  ):
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
												UpperCamelCase__       =							deepspeed.zero.GatheredParameters(snake_case   ,    modifier_rank=snake_case  )

									with context_manager():
												if param.requires_grad:
															s_param.sub_(one_minus_decay * (s_param - param)  )
												else:
															s_param.copy_(snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							list(snake_case  )
						for s_param, param in zip(self.shadow_params   ,    snake_case  ):
									param.data.copy_(s_param.to(param.device  ).data  )




			def 					snake_case__					(				self   ,    snake_case=None   ,    snake_case=None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[
						    p.to(device=snake_case   ,    dtype=snake_case  ) if p.is_floating_point() else p.to(device=snake_case  )
						    for p in self.shadow_params
						]




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return {
						    "decay": self.decay,
						    "min_decay": self.min_decay,
						    "optimization_step": self.optimization_step,
						    "update_after_step": self.update_after_step,
						    "use_ema_warmup": self.use_ema_warmup,
						    "inv_gamma": self.inv_gamma,
						    "power": self.power,
						    "shadow_params": self.shadow_params,
						}




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							[param.detach().cpu().clone() for param in parameters]




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if self.temp_stored_params is None:
									raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"  )
						for c_param, param in zip(self.temp_stored_params   ,    snake_case  ):
									param.data.copy_(c_param.data  )

						# Better memory-wise.
						UpperCamelCase__       =							None







			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							copy.deepcopy(snake_case  )

						UpperCamelCase__       =							state_dict.get("decay"   ,    self.decay  )
						if self.decay < 0.0 or self.decay > 1.0:
									raise ValueError("Decay must be between 0 and 1"  )

						UpperCamelCase__       =							state_dict.get("min_decay"   ,    self.min_decay  )
						if not isinstance(self.min_decay   ,    snake_case  ):
									raise ValueError("Invalid min_decay"  )

						UpperCamelCase__       =							state_dict.get("optimization_step"   ,    self.optimization_step  )
						if not isinstance(self.optimization_step   ,    snake_case  ):
									raise ValueError("Invalid optimization_step"  )

						UpperCamelCase__       =							state_dict.get("update_after_step"   ,    self.update_after_step  )
						if not isinstance(self.update_after_step   ,    snake_case  ):
									raise ValueError("Invalid update_after_step"  )

						UpperCamelCase__       =							state_dict.get("use_ema_warmup"   ,    self.use_ema_warmup  )
						if not isinstance(self.use_ema_warmup   ,    snake_case  ):
									raise ValueError("Invalid use_ema_warmup"  )

						UpperCamelCase__       =							state_dict.get("inv_gamma"   ,    self.inv_gamma  )
						if not isinstance(self.inv_gamma   ,    (float, int)  ):
									raise ValueError("Invalid inv_gamma"  )

						UpperCamelCase__       =							state_dict.get("power"   ,    self.power  )
						if not isinstance(self.power   ,    (float, int)  ):
									raise ValueError("Invalid power"  )

						UpperCamelCase__       =							state_dict.get("shadow_params"   ,    snake_case  )
						if shadow_params is not None:
									UpperCamelCase__       =							shadow_params
									if not isinstance(self.shadow_params   ,    snake_case  ):
												raise ValueError("shadow_params must be a list"  )
									if not all(isinstance(snake_case   ,    torch.Tensor  ) for p in self.shadow_params  ):
												raise ValueError("shadow_params must all be Tensors"  )






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		'xmod'




			def __init__(				self   ,    snake_case=30522   ,    snake_case=768   ,    snake_case=12   ,    snake_case=12   ,    snake_case=3072   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=1E-1_2   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    snake_case="absolute"   ,    snake_case=True   ,    snake_case=None   ,    snake_case=False   ,    snake_case=2   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=("en_XX",)   ,    snake_case=None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    **snake_case  )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							layer_norm_eps
						UpperCamelCase__       =							position_embedding_type
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							classifier_dropout
						UpperCamelCase__       =							pre_norm
						UpperCamelCase__       =							adapter_reduction_factor
						UpperCamelCase__       =							adapter_layer_norm
						UpperCamelCase__       =							adapter_reuse_layer_norm
						UpperCamelCase__       =							ln_before_adapter
						UpperCamelCase__       =							list(snake_case  )
						UpperCamelCase__       =							default_language







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__       =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__       =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]  )


